Textural analysis of lung nodules

ABSTRACT

Methods, apparatus, and other embodiments associated with classifying a region of tissue using textural analysis are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating GGO nodule pathology, a delineation logic that distinguishes GGO nodule tissue within the image from the background of the image, a texture logic that extracts a set of texture features from the image, a phenotype signature logic that computes a phenotypic signature from the image, a shape logic that extracts a set of shape features from the image, and a classification logic that classifies the GGO nodule tissue based, at least in part, on the set of texture features, the phenotypic signature, or the set of shape features. A prognosis for a patient may be provided based on the classification of the image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application62/085,616 filed Nov. 30, 2014.

BACKGROUND

Variations of lung nodule invasiveness and morphology relate toprognosis and patient outcomes. One approach for diagnosing cancer ishistopathological examination of biopsy tissue. The examination mayproduce a diagnostic profile based on attributes including cellmorphology, cytoplasmic changes, cell density, and cell distribution.Visual characterization of tumor morphology is, however, time consumingand expensive. Visual characterization is also subjective and thussuffers from inter-rater and intra-rater variability. Conventionalvisual characterization of lung nodule morphology by a human pathologistmay therefore be less than optimal in clinical situations where timelyand accurate classification can affect patient outcomes.

Computerized tomography (CT) is used to image nodules in lungs. Chest CTimagery may be used to detect and diagnose non-small cell lung cancer.However, conventional approaches have been challenged when definingradiographic characteristics that reliably describe the degree ofinvasion of early non-small cell lung cancers with ground glass opacity(GGO). For example, conventional CT imagery based approaches may find itdifficult, if even possible at all, to reliably discriminate nodulescaused by benign fungal infections from non-small cell lung cancernodules.

The degree of invasion of a lung nodule is correlated with prognosis.For example, patients suffering from minimally invasive nodules may havehigher disease free survival rate at five years compared to patientswith nodules demonstrating frank invasion. Since radiologists may bechallenged to reliably distinguish the level of invasiveness of lungnodules in situ using conventional CT approaches in clinically optimalor relevant time frames, invasive procedures that may be performed thatultimately result in a negative diagnosis. These invasive procedurestake time, cost money, and put a patient at additional risk.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example apparatus,methods, and other example embodiments of various aspects of theinvention. It will be appreciated that the illustrated elementboundaries (e.g., boxes, groups of boxes, or other shapes) in thefigures represent one example of the boundaries. One of ordinary skillin the art will appreciate that in some examples one element may bedesigned as multiple elements or that multiple elements may be designedas one element. In some examples, an element shown as an internalcomponent of another element may be implemented as an external componentand vice versa. Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates an example method of characterizing a GGO nodule in aregion of lung tissue.

FIG. 2 illustrates an example method of characterizing a GGO nodule in aregion of lung tissue.

FIG. 3 illustrates an example method of distinguishing invasive tumorsfrom non-invasive tumors in chest CT images.

FIG. 4 illustrates an example apparatus that classifies a region oftissue in an image.

FIG. 5 illustrates an example computer in which example methods andapparatus described herein operate.

FIG. 6 illustrates textural features of a CT image of a granuloma and acarcinoma.

FIG. 7 is a boxplot of CT intensities of a CT image of a minimallyinvasive GGO nodule and a frank invasive GGO nodule.

FIG. 8 is a boxplot of CT contrast variance of a CT image of minimallyinvasive GGO nodule and frank invasive GGO nodule.

DETAILED DESCRIPTION

Variations in tumor invasiveness and morphology may be related topatient prognosis and outcome. In particular, a GGO nodule's level ofinvasion is strongly correlated to patient prognosis. Conventionalmethods of diagnosing cancer include visual histopathologicalexamination of a biopsy to create a diagnostic profile based onvariations in tumor morphology. However, invasive biopsy may not alwaysbe a convenient or appropriate method for assessing GGO nodules.Invasive biopsies cost money, take time, and put a patient at additionalrisk. A non-invasive approach that provided improved accuracy comparedto conventional CT based approaches would reduce the number ofunnecessary interventions, reduce the dependency on repetitive or higherresolution CT exams, offer a non-invasive means of assessing response totargeted therapies, and improve patient outcomes. Thus, a timely,non-invasive procedure that results in more accurate discriminationbetween minimally invasive and frank invasive nodules would offerreduced risk to patients while providing economic benefits to the healthcare system.

CT imagery is conventionally used to differentiate malignant GGO nodulesfrom other, non-cancerous GGO nodules. Conventional methods of visuallyassessing GGO nodule invasiveness based on CT imagery are subjective andyield intra and inter-reviewer variability. In one example, of a groupof baseline CT chest scans, 51% were found positive for lung nodules.However, only 12% of those lung nodules were found to be malignant. Theremainder were determined to be granulomas due to a prior histoplasmosisinfection. Conventional CT approaches may focus exclusively on detectionof lung nodules, or exclusively on diagnosing malignancy via CT scans.Example apparatus and methods discriminate granulomas caused by fungalinfection from carcinomas. Distinguishing fungal infection fromcarcinoma facilitates reducing surgical interventions that ultimatelyresult in a diagnosis of histoplasmosis.

Example methods and apparatus more accurately distinguish malignant GGOnodules from benign nodules. Since a more accurate distinction is made,example apparatus and methods thus predict patient outcomes in a moreconsistent and reproducible manner. Example methods and apparatuspredict patient outcomes more accurately than conventional methods byemploying computerized textural and morphologic analysis of lung CTimagery to distinguish granulomas due to fungal infection from malignanttumors. A GGO nodule may be segmented from an image background. Featuresmay be automatically extracted from the segmented GGO nodule image.Example methods and apparatus may extract texture features and shapefeatures from the GGO nodule image. Example methods and apparatus mayalso extract tortuosity features from the GGO nodule image. Malignantlung tumors may induce irregular changes to vessel shapes. Examplemethods and apparatus detect and quantify vessel tortuosityabnormalities on a tumor neighborhood. A subset of extracted featuresmay be selected using principal component analysis (PCA) and then aclassification of the GGO nodule image may be generated using lineardiscriminant analysis (LDA) or quadratic discriminant analysis (QDA).

Carcinomas may have a more chaotic cellular architecture than granuloma.The chaotic cellular architecture may be correlated to an energy featurein an image. The energy feature may be represented as a texture featureor a shape feature. In some embodiments, the energy feature is morepronounced in a CT heatmap of a cancerous GGO nodule than in a CTheatmap of a granuloma because of the more chaotic cellular architectureof the cancerous GGO nodule. FIG. 6 illustrates this property ofcancerous GGO nodules compared with granuloma GGO nodules that werecaused by benign fungal infections. The chaotic cellular architecturemay also be correlated to tortuosity features of vessels associated witha tumor or a GGO nodule.

FIG. 6 illustrates, at 610, a CT scan image of a cancerous GGO noduleidentified as a carcinoma. FIG. 6 also illustrates, at 620, a CT scanimage of GGO nodule identified as a granuloma. FIG. 6 illustrates, at630, an energy textural feature extracted from intensity values of image610 of the cancerous GGO nodule. The energy of a textural featuremeasures local homogeneity within the image, illustrating how uniformthe texture is. FIG. 6 also illustrates a textural feature image 640 ofthe energy extracted from the image of the benign granuloma 620. Theenergy feature image 630 of the cancerous GGO nodule displays a morepronounced energy feature than the benign granuloma texture featureimage 640, which demonstrates the more chaotic cellular architecture ofa cancerous GGO nodule. FIG. 6 also illustrates, at 650, a heatmap of aGabor feature of the cancerous GGO nodule. The Gabor feature representstexture using a sinusoidal plane wave modulated Gaussian kernelfunction. FIG. 6 further illustrates, at 660, a heatmap of a Gabortexture feature of the benign granuloma.

Example methods and apparatus may also employ 3-fold cross validationwhere N=46 for training a classifier and N=16 for testing a classifier.Example methods and apparatus may train a classifier or test aclassifier with other, different numbers of subjects. For example, ahuman pathologist may manually delineate and classify one hundred GGOnodules for a training set and thirty nodules for a testing set. Examplemethods and apparatus may classify the GGO nodule image as a carcinoma,adenocarcinoma, or as a granuloma. Example methods and apparatus mayalso classify the GGO nodule image as non-invasive, minimally invasive,or frank invasive. Other classifications may be employed.

Example methods and apparatus thus improve on conventional methods bymore accurately distinguishing between pathological and benign lungnodules.

Example methods and apparatus distinguish granuloma from carcinoma withan accuracy of at least 92% area under the curve (AUC) when usingtexture features and shape features with a linear discriminant analysis(LDA) classifier. Example methods and apparatus distinguish frankinvasive GGO nodules from non-invasive or minimally invasive GGO noduleswith an accuracy of at least 78% AUC when using three texture featuresselected by PCA with a quadratic discriminant analysis (QDA) classifier.In contrast, conventional approaches using just Laws feature achieveaccuracies of approximately 0.61 AUC, while conventional approachesusing just Gabor features achieve accuracies of approximately 0.68 AUC.In these examples, a minimally invasive GGO nodule is defined as a GGOnodule with 5 mm or less invasion, and a frank invasive GGO nodule isdefined as a GGO nodule with more than 5 mm invasion. In otherembodiments, minimally invasive GGO nodules and frank invasive GGOnodules may be defined on other dimensions.

By increasing the accuracy with which malignant GGO nodules aredistinguished from benign lung GGO nodules, example methods andapparatus produce the concrete, real-world technical effect of reducingthe time required to evaluate medical imagery while increasing theaccuracy of the evaluation. Additionally, example apparatus and methodsincrease the probability that at-risk patients receive timely treatmenttailored to the particular pathology they exhibit. Example methods andapparatus may also reduce the number of invasive procedures needed toaccurately characterize GGO nodules. The additional technical effect ofreducing the expenditure of resources and time on patients who are lesslikely to suffer recurrence or disease progression is also achieved.Example methods and apparatus thus improve on conventional methods in ameasurable, clinically significant way.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic, and so on. The physicalmanipulations create a concrete, tangible, useful, real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, calculating, determining, and so on, refer to actions andprocesses of a computer system, logic, processor, or similar electronicdevice that manipulates and transforms data represented as physical(electronic) quantities.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

FIG. 1 illustrates an example computerized method 100 for characterizinga GGO nodule in a region of lung tissue. Method 100 includes, at 110,accessing an image of a region of lung tissue. Accessing the image mayinclude accessing a CT image of the region of lung tissue. The CT imagemay be stored, for example, in a computer memory or may be providedacross a computer network. In one embodiment, the CT image is a 1 mm to5 mm thick, no-contrast chest CT image. In another embodiment, otherimages sizes or other imaging techniques may be employed.

Method 100 also includes, at 120, delineating a GGO nodule in the image.The GGO nodule may be automatically delineated by distinguishing GGOnodule tissue within the image from the background of the image. The GGOnodule tissue may be automatically distinguished using threshold basedsegmentation, deformable boundary models, active-appearance models,active shape models, graph based models including Markov random fields(MRF), min-max cut approaches, or other image segmentation approaches.

Method 100 also includes, at 130, extracting a set of texture featuresfrom the image of the GGO nodule. The set of texture features includes agray-level statistical feature, a steerable Gabor feature, a Haralickfeature, a Law feature, a Law-Laplacian feature, a local binary pattern(LBP) feature, inertia, a correlation feature, a difference entropyfeature, a contrast inverse moment feature, or a contrast variancefeature. In one embodiment, the set of texture features includes atleast sixty three texture features. In another embodiment, the set oftexture features includes at least one hundred texture features. Forexample, a set of one hundred texture features may include 13 Haralickfeatures, 4 gray features, 13 gradient features, 19 Gabor features, 1LBP feature, 25 Law features, or 25 Law-Laplacian features. In otherembodiments, other numbers or types of texture features may beextracted.

Method 100 also includes, at 140, selecting a subset of texture featuresfrom the set of texture features. In one embodiment, the subset oftexture features is selected by reducing the set of texture featuresusing a PCA. The PCA of the set of texture features selects a subset oftexture features from the set of texture features. The subset of texturefeatures achieves a threshold level of discriminability. For example,the PCA may select one energy feature and one Gabor feature that are themost discriminative, based on a particular set of CT images, fordistinguishing carcinoma from granuloma. The subset of texture featuresmay include as few as two texture features. The level ofdiscriminability may be user adjustable. For example, in a firstclinical situation, a subset of texture features that achieves 0.8 AUCaccuracy in distinguishing carcinoma from granuloma may be acceptable. Afeature may be considered to have a desirable level of discriminabilitywhen the means of two separate classes are more than a thresholddistance from each other, and where the variance of a class is less thana threshold distance, in comparison to the distance between the means.In one embodiment, the Fisher criterion, which is the squared differenceof the means divided by the sum of the variances, may be used toquantitatively establish a desirable level of discriminability.

FIG. 7 is a boxplot of CT intensities of a CT image of a minimallyinvasive GGO nodule and a frank invasive GGO nodule. In FIG. 7, theY-axis indicates the normalized value of the average Hounsfield unitwithin a nodule in the CT image. FIG. 7 illustrates the challengeinvolved with classifying GGO nodules using conventional CT imageryapproaches. FIG. 7 demonstrates that the CT intensities of a minimallyinvasive GGO nodule and frank invasive GGO nodule overlap significantly.In this example, the median of the CT intensity for the minimallyinvasive GGO nodule is within a threshold distance from the median ofthe frank invasive GGO. Thus, an attempt to classify a GGO nodule basedon just CT intensity is unlikely to be acceptably accurate.

FIG. 8 is a boxplot of CT contrast variance of a CT image of a minimallyinvasive GGO nodule and a frank invasive GGO nodule. In FIG. 8, theY-axis indicates the normalized value of the average Hounsfield unitwith a nodule in the CT image. FIG. 8 demonstrates how texture features,like contrast variance, may have a more desirable level ofdiscriminability. In this example, the median of the CT contrastvariance for the minimally invasive GGO nodule is more than a thresholddistance from the median of the CT contrast variance for the frankinvasive GGO.

Method 100 also includes, at 150, generating a phenotypic signature forthe nodule. In one embodiment, the phenotypic signature is generatedusing Fisher criteria ranking. In another embodiment, the phenotypicsignature is generated using other techniques.

Method 100 also includes, at 160, controlling a computer aided diagnosis(CADx) system to generate a classification of the GGO nodule in theimage. The classification may be based, at least in part, on the subsetof texture features or the phenotypic signature. In one embodiment, theCADx system generates the classification of the image of the GGO noduleusing a QDA classifier. In another embodiment, the CADx system maygenerate the classification using other, different types of classifier.The classifier may be trained and tested on a set of images ofpre-classified GGO nodules. In one embodiment, the image is of a regionof tissue demonstrating adenocarcinoma pathology. Controlling the CADxsystem to generate the classification of the GGO nodule based, at leastin part, on the subset of texture features and the phenotypic signature,includes classifying the image of the GGO nodule as frank invasiveadenocarcinoma or minimally invasive adenocarcinoma.

Example methods and apparatus facilitate more accurate characterizationof GGO nodules found in CT images than conventional approaches. Examplemethods and apparatus thus improve on conventional methods bycharacterizing GGO nodules as frank invasive, non-invasive, or minimallyinvasive, or as carcinomas, adenocarcinomas, or granulomas with greateraccuracy and with less subjective variability than conventional methods.Example methods and apparatus therefore facilitate more judiciousapplication of biopsies and surgical resection in a populationundergoing CT screening for lung cancer.

Using a more appropriately determined and applied treatment may lead toless therapeutics being required for a patient or may lead to avoidingor delaying a biopsy, a resection, or other invasive procedure. Whenregions of cancerous tissue, including GGO nodules detected in CT scans,are more quickly and more accurately classified, patients with poorerprognoses may receive a higher proportion of scarce resources (e.g.,therapeutics, physician time and attention, hospital beds) while thosewith better prognoses may be spared unnecessary treatment, which in turnspares unnecessary expenditures and resource consumption. Examplemethods and apparatus may thus have the real-world, quantifiable effectof improving patient outcomes.

While FIG. 1 illustrates various actions occurring in serial, it is tobe appreciated that various actions illustrated in FIG. 1 could occursubstantially in parallel. By way of illustration, a first process coulddelineate a GGO nodule in a CT image, a second process could extracttexture features from the CT image, and a third process could extractshape features from the CT image. While three processes are described,it is to be appreciated that a greater or lesser number of processescould be employed and that lightweight processes, regular processes,threads, and other approaches could be employed.

FIG. 2 illustrates an example method 200 for characterizing a GGO nodulein a region of lung tissue. Method 200 is similar to method 100 butincludes additional actions. Method 200 includes actions 210, 220, 230,240, and 250 which are similar to actions 110, 120, 130, 140, and 150described above with respect to method 100.

Method 200 also includes, at 260, extracting a set of shape featuresfrom the image of the GGO nodule. The set of shape features includes alocation feature, a size feature, a width feature, a height feature, adepth feature, a perimeter feature, an eccentricity feature, aneccentricity standard deviation, a compactness feature, a roughnessfeature, an elongation feature, a convexity feature, an extend feature,an equivalent diameter feature, or a sphericity feature. The locationfeature describes the spatial information of a pixel in the image of theGGO nodule, the size feature describes the number of pixels within thesegmented image of the GGO nodule, and the perimeter feature describesthe distance around the boundary of the segmented GGO nodule. Theeccentricity feature describes the eccentricity of an ellipse that hasthe same second moments as the nodule. The compactness feature describesthe isoperimetric quotient of the nodule. The roughness featuredescribes the perimeter of a lesion in a slice of the image of the GGOnodule divided by the convex perimeter of the lesion. The elongationfeature describes the ratio of minor axis to the major axis of the imageof the GGO nodule, and the convexity feature describes the ratio of atumor image slice to the convex hull of the tumor. The extend featuredescribes the ratio of pixels in the tumor region to pixels in the totalbounding box. The equivalent diameter feature describes the diameter ofa circle having the same area as a tumor image slice, and the sphericityfeature describes the three-dimensional compactness of the nodule. Inone embodiment the set of shape features includes at least twenty-fiveshape features. In another embodiment, the set of shape features mayinclude other numbers of shape features, or other, different shapefeatures. A feature may be calculated in three dimensional (3D) space,or in two dimensional (2D) space. For example, width, height, depth, orsphericity features may be calculated in 3D space.

Method 200 also includes, at 270, selecting a subset of shape featuresfrom the set of shape features. In one embodiment, the subset of shapefeatures includes eccentricity, eccentricity standard deviation, orelongation features. In another embodiment, the subset of shape featuresmay include other, different shape features. The subset of shapefeatures may be selected from the set of shape features using PCA.

Method 200 also includes, at 280, controlling the CADx system togenerate the classification of the image of the GGO nodule as acarcinoma or a granuloma. The classification may be based, at least inpart, on the subset of texture features and the subset of shapefeatures. Basing the classification on both the subset of texturefeatures and the subset of shape features improves on conventionalapproaches by increasing the accuracy with which the image of the GGOmay be classified. In one embodiment, the CADx system generates theclassification of the image of the GGO nodule using a LDA classifier ora QDA classifier. In one embodiment, an LDA classifier using a mediantextural feature and eccentricity standard deviation shape featureachieves an accuracy of at least 0.92 AUC. The LDA classifier or the QDAclassifier may be trained and tested on a set of GGO imagespre-classified as carcinoma or granuloma.

In one embodiment, method 200 may also automatically segment vesselsassociated with the nodule. Method 200 may identify a centerline of avessel and branching points associated with the vessel. Method 200calculates the torsion for a vessel segment using a distance metric. Thetorsion of a vessel segment is defined as 1−(Distance/Length) wheredistance is the Euclidean distance of the start and end point of thesegment, and where length is the number of voxels along the vesselsegment. Method 200 also extracts the curvature of a vessel segment.Curvature at a voxel of a vessel segment is proportional to the inverseof an osculating circle's radius. The osculating circle is fitted to acollection of three neighboring points along the centerline of a vessel.For a plurality of points along the center line of a vessel, method 200fits a circle to compute the curvature of a specific point. Method 200then computes mean and standard deviation of the curvature for pointsalong the vessel.

Method 200 may then extract a set of tortuosity features from the imageof the GGO nodule. The tortuosity features describe vessels associatedwith the GGO nodule. The set of tortuosity features includes the mean oftorsion of a vessel segment, or the standard deviation of torsion of avessel segment. The set of tortuosity features also includes the meanand standard deviation of the mean curvature of a group of vesselsegments. The set of tortuosity features also includes the mean andstandard deviation of the standard deviation of a vessel segmentcurvature and a total vessel segment length. In one embodiment, the setof tortuosity features includes at least seven tortuosity features. Inanother embodiment, the set of tortuosity features may include othernumbers of tortuosity features, or other, different tortuosity features.Method 200 may also select of subset of tortuosity features from the setof tortuosity features. Method 200 may also include controlling the CADxsystem to generate the classification of the image of the GGO nodulebased, at least in part, on the subset of tortuosity features, thesubset of texture features and the subset of shape features.

FIG. 3 illustrates an example method 300 for distinguishing invasivetumors from non-invasive tumors in chest CT images. Method 300 includes,at 310 accessing an image of a region of tissue demonstrating cancerouspathology. In one embodiment, the image is a 1 mm to 5 mm thick,no-contrast chest CT image. In another embodiment, other image types orimage dimensions may be used. Accessing the image may include retrievingelectronic data from a computer memory, receiving a computer file over acomputer network, or other computer or electronic based action.

Method 300 also includes, at 320, segmenting a tumor in the image fromthe background of the image. Segmenting the tumor in the image from thebackground of the image involves identifying the portion of the imagethat represents the tumor to distinguish that portion from thebackground. In one embodiment, the tumor is automatically segmented fromthe background of the image. In another embodiment, a human pathologistmanually delineates the tumor from the background of the image. Inanother embodiment, vessels associated with the tumor are alsosegmented.

Method 300 also includes, at 330, selecting a set of texture featuresfrom the segmented image. In one embodiment, the set of texture featuresmay include a gray-level statistical feature, a steerable Gabor feature,a Haralick feature, a Law feature, a Law-Laplacian feature, an LBPfeature, an inertia feature, a correlation feature, a difference entropyfeature, a contrast inverse moment feature, or a contrast variancefeature. In another embodiment, other, different texture features may beselected. The inertia feature describes the contrast or local intensityvariation of the segmented image. The correlation feature describes thecorrelation of the intensity of values within the segmented image. Thedifference entropy feature describes the disorder of the differencebetween a pair of pixel intensities within the segmented image. Thecontrast inverse moment feature describes the inhomogeneity within aregion of interest in the segmented image. The contract variance featuredescribes the variance of the difference between a pair of pixelintensities.

Method 300 also includes, at 340, selecting a set of shape features fromthe segmented image. The set of shape features may include a locationfeature, a size feature, a perimeter feature, an eccentricity feature,an eccentricity standard deviation, a compactness feature, a roughnessfeature, an elongation feature, a convexity feature, an equivalentdiameter feature, a radial distance feature, an area feature, or asphericity feature. The radial distance feature describes the radialdistance from the center of mass of the tumor to a point on the definingcontour of the tumor.

Method 300 also includes, at 345, selecting a set of tortuosity featuresfrom the segmented image. The set of tortuosity features may include themean of torsion of a vessel segment, or the standard deviation oftorsion of a vessel segment. The set of tortuosity features may alsoinclude the mean and standard deviation of the mean curvature of a groupof vessel segments. The set of tortuosity features may also include themean and standard deviation of the standard deviation of a vesselsegment curvature and a total vessel segment length. In one embodiment,the set of tortuosity features includes at least seven tortuosityfeatures. In another embodiment, the set of tortuosity features mayinclude other numbers of tortuosity features, or other, differenttortuosity features.

Method 300 also includes, at 350, generating a classification for thetumor based, at least in part, on the set of texture features, the setof shape features, and the set of tortuosity features. In oneembodiment, the classification is made based on the set of texturefeatures. In another embodiment, the classification is based on the setof shape features. In still another embodiment, the classification isbased on a subset of the set of texture features, a subset of the set ofshape features, and a subset of the set of tortuosity features. Thesubset of the set of texture features may be selected from the set oftexture features using PCA. The subset of the set of shape features maybe selected from the set of shape features using PCA. The subset of theset of tortuosity features may be selected from the set of tortuosityfeatures using PCA. The subset of shape features, the subset of texturefeatures, or the subset of tortuosity features may be selected toachieve a threshold level of accuracy when classifying tumors. In oneembodiment, method 300 classifies the tumor as a carcinoma or agranuloma. In another embodiment, the tumor is classified as frankinvasive, minimally invasive, or non-invasive. The classification may bemade by a CADx system using a QDA classifier or an LDA classifier.

Method 300 also includes, at 360, providing a prognosis prediction basedon the classification. For example, method 300 may, at 360, provide aprobability that a patient will experience a lower five year survivalrate if the tumor is classified as frank invasive. Method 300 mayalternately provide a probability that a patient will experience ahigher five year survival rate if the tumor is classified asnon-invasive.

In one example, a method may be implemented as computer executableinstructions. Thus, in one example, a computer-readable storage mediummay store computer executable instructions that if executed by a machine(e.g., computer) cause the machine to perform methods described orclaimed herein including method 100, method 200, and method 300. Whileexecutable instructions associated with the listed methods are describedas being stored on a computer-readable storage medium, it is to beappreciated that executable instructions associated with other examplemethods described or claimed herein may also be stored on acomputer-readable storage medium. In different embodiments the examplemethods described herein may be triggered in different ways. In oneembodiment, a method may be triggered manually by a user. In anotherexample, a method may be triggered automatically.

FIG. 4 illustrates an example apparatus 400 for classifying a region oftissue in an image. Apparatus 400 includes a processor 410, a memory420, a set of logics 440, and an interface 430 that connects theprocessor 410, the memory 420, and the set of logics 440. The set oflogics 440 includes an image acquisition logic 441, a delineation logic443, a texture logic 445, a phenotype signature logic 446, a shape logic447, and a classification logic 449. In one embodiment, thefunctionality associated with the set of logics 440 may be performed, atleast in part, by hardware logic components including, but not limitedto, field-programmable gate arrays (FPGAs), application specificintegrated circuits (ASICs), application specific standard products(ASSPs), system on a chip systems (SOCs), or complex programmable logicdevices (CPLDs). In one embodiment, individual members of the set oflogics 440 are implemented as ASICs or SOCs.

Image acquisition logic 441 acquires an image of a region of tissue. Theimage may be acquired from, for example, a CT apparatus. The region oftissue may be a section of tissue demonstrating cancerous pathology in apatient. The image of the region of tissue may include an image of a GGOnodule. In one embodiment, the image is a 1 mm to 5 mm thick,no-contrast chest CT image. Other imaging approaches may be used togenerate and access the image accessed by image acquisition logic 441.Other image dimensions may also be used.

Delineation logic 443 automatically delineates the GGO nodule bydistinguishing GGO nodule tissue within the image from the background ofthe image. Delineation logic 443 automatically delineates the GGO noduleusing threshold based segmentation, deformable boundary models,active-appearance models, active shape models, graph based modelsincluding Markov random fields (MRF), min-max cut approaches, or otherimage segmentation approaches.

Texture logic 445 extracts a set of texture features from the image. Theset of texture features may be extracted from the image of thedelineated GGO nodule. In one embodiment, the set of texture featuresincludes a gray-level statistical feature, a steerable Gabor feature, aHaralick feature, a Law feature, a Law-Laplacian feature, an LBPfeature, inertia, correlation, difference entropy, contrast inversemoment, or contrast variance. The texture logic 445 may also select asubset of texture features from the set of texture features. Texturelogic 445 may select the subset of texture features based on, at leastin part, a PCA of the set of texture features.

Phenotype selection logic 446 computes a phenotypic signature of thedelineated GGO nodule in the image. Phenotype selection logic 446 maycompute the phenotypic signature using a Fisher criteria ranking.

Shape logic 447 extracts a set of shape features from the image. The setof shape features may include a location feature, a size feature, aperimeter feature, an eccentricity feature, an eccentricity standarddeviation, a compactness feature, a roughness feature, an elongationfeature, a convexity feature, an equivalent diameter feature, or asphericity feature. Shape logic 447 also selects a subset of shapefeatures from the set of shape features based, at least in part, on aPCA of the set of shape features.

Classification logic 449 classifies the GGO nodule tissue based, atleast in part, on the set of texture features, the phenotypic signature,or the set of shape features. In one embodiment, classification logic449 logic classifies the GGO nodule tissue as a carcinoma or a granulomausing an LDA of the subset of texture features and the subset of shapefeatures. In another embodiment, classification logic 449 classifies theGGO nodule tissue as minimally invasive or as frank invasive using a QDAof the subset of texture features. In still another embodiment,classification logic 449 may classify the GGO nodule tissue using otheranalytical techniques.

In another embodiment, classification logic 449 may control a CADxsystem to classify the image based, at least in part, on theclassification. For example, classification logic 449 may control a lungcancer CADx system to classify the image based, at least in part, on theset of texture features and set of shape features. In other embodiments,other types of CADx systems may be controlled, including CADx systemsfor distinguishing GGO nodules among oral cancer, prostate cancer, coloncancer, brain cancer, and other diseases where disease classificationand prognosis prediction may be based on textural or shape featuresquantified from CT images of a GGO nodule.

In one embodiment of apparatus 400, the set of logics 440 also includesa tortuosity logic. The tortuosity logic identifies a vessel associatedwith the GGO nodule. The tortuosity logic identifies the centerline anda branching point of the vessel associated with the GGO nodule. Thetortuosity logic computes a torsion for the segment of the vessel. Thetortuosity logic also computes a curvature of a voxel of a vesselsegment, where the curvature is proportional to the inverse of anosculating circle's radius. The tortuosity logic extracts a set oftortuosity features from the image. The set of tortuosity features mayinclude the mean of torsion of a vessel segment, or the standarddeviation of torsion of a vessel segment. The set of tortuosity featuresalso may include the mean and standard deviation of the mean curvatureof a group of vessel segments. The set of tortuosity features also mayinclude the mean and standard deviation of the standard deviation of avessel segment curvature and a total vessel segment length. Thetortuosity logic also selects a subset of tortuosity features from theset of tortuosity features based, at least in part, on a PCA of the setof tortuosity features. The subset of tortuosity features may include atleast three tortuosity features. In this embodiment, the classificationlogic 449 classifies the GGO nodule tissue based, at least in part, onthe set of tortuosity features, the set of texture features, thephenotypic signature, or the set of shape features.

In one embodiment of apparatus 400, the set of logics 440 also includesa display logic. The display logic may control the CADx system todisplay the classification, the texture features, or the shape featureson a computer monitor, a smartphone display, a tablet display, or otherdisplays. Displaying the classification or the features may also includeprinting the classification or the features. The display logic may alsocontrol the CADx to display an image of the region of tissuedemonstrating a GGO nodule. The image of the region of tissuedemonstrating a GGO nodule may include a delineated or segmentedrepresentation of the GGO nodule. By displaying the features and theimage of the GGO nodule, example apparatus provide a timely andintuitive way for a human pathologist to more accurately classifypathologies demonstrated by a patient, thus improving on conventionalapproaches to predicting cancer recurrence and disease progression.

FIG. 5 illustrates an example computer 500 in which example methodsillustrated herein can operate and in which example logics may beimplemented. In different examples, computer 500 may be part of a CTsystem, may be operably connectable to a CT system, or may be part of aCADx system.

Computer 500 includes a processor 502, a memory 504, and input/outputports 510 operably connected by a bus 508. In one example, computer 500may include a set of logics 530 that perform a method of characterizinga GGO nodule in a region of lung tissue. Thus, the set of logics 530,whether implemented in computer 500 as hardware, firmware, software,and/or a combination thereof may provide means (e.g., hardware,software) for characterizing a GGO nodule in a region of lung tissue. Indifferent examples, the set of logics 530 may be permanently and/orremovably attached to computer 500. In one embodiment, the functionalityassociated with the set of logics 530 may be performed, at least inpart, by hardware logic components including, but not limited to,field-programmable gate arrays (FPGAs), application specific integratedcircuits (ASICs), application specific standard products (ASSPs), systemon a chip systems (SOCs), or complex programmable logic devices (CPLDs).In one embodiment, individual members of the set of logics 530 areimplemented as ASICs or SOCs.

Processor 502 can be a variety of various processors including dualmicroprocessor and other multi-processor architectures. Memory 504 caninclude volatile memory and/or non-volatile memory. A disk 506 may beoperably connected to computer 500 via, for example, an input/outputinterface (e.g., card, device) 518 and an input/output port 510. Disk506 may include, but is not limited to, devices like a magnetic diskdrive, a tape drive, a Zip drive, a flash memory card, or a memorystick. Furthermore, disk 506 may include optical drives like a CD-ROM ora digital video ROM drive (DVD ROM). Memory 504 can store processes 514or data 517, for example. Disk 506 or memory 504 can store an operatingsystem that controls and allocates resources of computer 500.

Bus 508 can be a single internal bus interconnect architecture or otherbus or mesh architectures. While a single bus is illustrated, it is tobe appreciated that computer 500 may communicate with various devices,logics, and peripherals using other busses that are not illustrated(e.g., PCIE, SATA, Infiniband, 1394, USB, Ethemet).

Computer 500 may interact with input/output devices via I/O interfaces518 and input/output ports 510. Input/output devices can include, butare not limited to, digital whole slide scanners, an optical microscope,a keyboard, a microphone, a pointing and selection device, cameras,video cards, displays, disk 506, network devices 520, or other devices.Input/output ports 510 can include but are not limited to, serial ports,parallel ports, or USB ports.

Computer 500 may operate in a network environment and thus may beconnected to network devices 520 via I/O interfaces 518 or I/O ports510. Through the network devices 520, computer 500 may interact with anetwork. Through the network, computer 500 may be logically connected toremote computers. The networks with which computer 500 may interactinclude, but are not limited to, a local area network (LAN), a wide areanetwork (WAN), or other networks.

References to “one embodiment”, “an embodiment”. “one example”, and “anexample” indicate that the embodiment(s) or example(s) so described mayinclude a particular feature, structure, characteristic, property,element, or limitation, but that not every embodiment or examplenecessarily includes that particular feature, structure, characteristic,property, element or limitation. Furthermore, repeated use of the phrase“in one embodiment” does not necessarily refer to the same embodiment,though it may.

“Computer-readable storage medium”, as used herein, refers to a mediumthat stores instructions or data. “Computer-readable storage medium”does not refer to propagated signals. A computer-readable storage mediummay take forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, tapes, and other media. Volatile media mayinclude, for example, semiconductor memories, dynamic memory, and othermedia. Common forms of a computer-readable storage medium may include,but are not limited to, a floppy disk, a flexible disk, a hard disk, amagnetic tape, other magnetic medium, an application specific integratedcircuit (ASIC), a compact disk (CD), other optical medium, a randomaccess memory (RAM), a read only memory (ROM), a memory chip or card, amemory stick, and other media from which a computer, a processor orother electronic device can read.

“Logic”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, or combinations of each toperform a function(s) or an action(s), or to cause a function or actionfrom another logic, method, or system. Logic may include a softwarecontrolled microprocessor, a discrete logic (e.g., ASIC), an analogcircuit, a digital circuit, a programmed logic device, a memory devicecontaining instructions, and other physical devices. Logic may includeone or more gates, combinations of gates, or other circuit components.Where multiple logical logics are described, it may be possible toincorporate the multiple logical logics into one physical logic.Similarly, where a single logical logic is described, it may be possibleto distribute that single logical logic between multiple physicallogics.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

Throughout this specification and the claims that follow, unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood to beterms of inclusion and not exclusion. For example, when such terms areused to refer to a stated integer or group of integers, such terms donot imply the exclusion of any other integer or group of integers.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

While example systems, methods, and other embodiments have beenillustrated by describing examples, and while the examples have beendescribed in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the systems, methods, and other embodiments described herein.Therefore, the invention is not limited to the specific details, therepresentative apparatus, and illustrative examples shown and described.Thus, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims.

What is claimed is:
 1. A non-transitory computer-readable storage mediumstoring computer executable instructions that when executed by acomputer control the computer to perform a method for characterizing aground glass (GGO) nodule in a region of lung tissue, the methodcomprising: accessing an image of a region of lung tissue; delineating aGGO nodule in the image; extracting a set of texture features from theGGO nodule; selecting a subset of texture features from the set oftexture features; extracting a set of shape features from the GGOnodule; selecting a subset of shape features from the set of shapefeatures; generating a phenotypic signature for the nodule; andcontrolling a computer aided diagnosis (CADx) system to generate aclassification of the GGO nodule in the image based, at least in part,on the subset of texture features, the subset of shape features, or thephenotypic signature.
 2. The non-transitory computer-readable storagemedium of claim 1, where accessing the image of the region of lungtissue includes accessing a computed tomography (CT) image of the regionof lung tissue, where the CT image is a no-contrast chest CT image. 3.The non-transitory computer-readable storage medium of claim 1, themethod comprising automatically delineating the GGO nodule bydistinguishing GGO nodule tissue in the image from the background of theimage.
 4. The non-transitory computer-readable storage medium of claim1, where the set of texture features includes at least sixty threetexture features.
 5. The non-transitory computer-readable storage mediumof claim 1, where the set of texture features includes a gray-levelstatistical feature, a steerable Gabor feature, a Haralick feature, aLaw feature, a Law-Laplacian feature, an LBP feature, an inertiafeature, a correlation feature, a difference entropy feature, a contrastinverse moment feature, a gradient feature, or a contrast variancefeature.
 6. The non-transitory computer-readable storage medium of claim1, where selecting the subset of texture features from the set oftexture features includes reducing the set of texture features usingprincipal component analysis (PCA).
 7. The non-transitorycomputer-readable storage medium of claim 1, where the phenotypicsignature is generated using Fisher criteria ranking.
 8. Thenon-transitory computer-readable storage medium of claim 1, where theCADx system generates the classification of the image of the GGO noduleusing a quadratic discriminant analysis (QDA) classifier.
 9. Thenon-transitory computer-readable storage medium of claim 1, where theimage is of a region of adenocarcinoma tissue, and where controlling theCADx system to generate the classification of the image of the GGOnodule based, at least in part, on the subset of texture features andthe phenotypic signature, includes classifying the image of the GGOnodule as frank invasive adenocarcinoma or minimally invasiveadenocarcinoma.
 10. The non-transitory computer-readable storage mediumof claim 1, where the set of shape features includes a location feature,a size feature, a width feature, a height feature, a depth feature, aradial distance feature, a perimeter feature, an eccentricity feature,an eccentricity standard deviation, a compactness feature, a roughnessfeature, an elongation feature, a convexity feature, an equivalentdiameter feature, or a sphericity feature.
 11. The non-transitorycomputer-readable storage medium of claim 10, where the subset of shapefeatures includes an eccentricity feature, an eccentricity standarddeviation feature, or an elongation feature.
 12. The non-transitorycomputer-readable storage medium of claim 11, the method comprisingcontrolling the CADx system to generate the classification of the imageof the GGO nodule as a carcinoma or a granuloma based, at least in part,on the subset of texture features and the subset of shape features. 13.The non-transitory computer-readable storage medium of claim 12, themethod further comprising: segmenting a vessel associated with the imageof the GGO nodule into a plurality of vessel segments; computing atorsion for a vessel segment; computing a curvature for the vesselsegment, where the curvature is proportional to the inverse of theradius of an osculating circle; selecting a set of tortuosity featuresfrom the image of the GGO nodule, where the set of tortuosity featuresincludes a mean of torsion of the vessel segment, a standard deviationof torsion of the vessel segment, a mean of the mean curvature of theplurality of vessel segments, a standard deviation of the mean curvatureof the plurality of vessel segments, a mean of the standard deviation ofthe curvature of the vessel segment, a standard deviation of thestandard deviation of the curvature of the vessel segment, a totallength of a vessel segment, or a total length of the plurality of vesselsegments; selecting a subset of tortuosity features from the set oftortuosity features using a PCA of the set of tortuosity features; andcontrolling a computer aided diagnosis (CADx) system to generate aclassification of the GGO nodule in the image based, at least in part,on the subset of tortuosity features, the subset of texture features,the subset of shape features, or the phenotypic signature.
 14. Thenon-transitory computer-readable storage medium of claim 1, where theCADx system generates the classification of the image of the GGO noduleusing a linear discriminant analysis (LDA) classifier or a quadraticdiscriminant analysis (QDA) classifier.
 15. The non-transitorycomputer-readable storage medium of claim 14, where the LDA classifierclassifies the image of the GGO nodule with an accuracy of at least 0.92area under the curve (AUC).
 16. An apparatus for classifying a region oftissue in an image, comprising: a processor; a memory; an input/outputinterface; a set of logics; and an interface to connect the processor,the memory, the input/output interface and the set of logics, where theset of logics includes: an image acquisition logic that acquires animage of a region of tissue demonstrating ground glass (GGO) nodulepathology; a delineation logic that distinguishes GGO nodule tissue inthe image from the background of the image; a texture logic thatextracts a set of texture features from the image; a phenotype signaturelogic that computes a phenotypic signature from the image; a shape logicthat extracts a set of shape features from the image; and aclassification logic that classifies the GGO nodule tissue based, atleast in part, on the set of texture features, the phenotypic signature,or the set of shape features.
 17. The apparatus of claim 16, where theset of texture features includes a gray-level statistical feature, asteerable Gabor feature, a Haralick feature, a Law feature, aLaw-Laplacian feature, a gradient feature, a local binary pattern (LBP)feature, an inertia feature, a correlation feature, a difference entropyfeature, a contrast inverse moment feature, or a contrast variancefeature, and where the texture logic selects a subset of texturefeatures from the set of texture features based on, at least in part, aprincipal component analysis (PCA) of the set of texture features. 18.The apparatus of claim 17, where the set of shape features includes alocation feature, a size feature, a width feature, a height feature, adepth feature, a radial distance feature, a perimeter feature, aneccentricity feature, an eccentricity standard deviation feature, acompactness feature, a roughness feature, an elongation feature, aconvexity feature, an equivalent diameter feature, or a sphericityfeature, and where the shape logic selects a subset of shape featuresfrom the set of shape features based on, at least in part, a PCA of theset of shape features.
 19. The apparatus of claim 18, where theclassification logic classifies the GGO nodule tissue as a carcinoma ora granuloma using a linear discriminant analysis of the subset oftexture features and the subset of shape features, or where theclassification logic classifies the GGO nodule tissue as minimallyinvasive or as frank invasive using a quadratic discriminant analysis ofthe subset of texture features.
 20. The apparatus of claim 19, the setof logics comprising a tortuosity logic that extracts a set oftortuosity features from the image, the set of tortuosity featuresincluding a mean of torsion of the vessel segment, the standarddeviation of torsion of the vessel segment, a mean of the mean curvatureof the plurality of vessel segments, a standard deviation of the meancurvature of the plurality of vessel segments, a mean of the standarddeviation of the curvature of the vessel segment, a standard deviationof the standard deviation of the curvature of the vessel segment, atotal length of a vessel segment, or a total length of the plurality ofvessel segments.
 21. The apparatus of claim 20, where the classificationlogic classifies the GGO nodule tissue based, at least in part, on theset of tortuosity features, the set of texture features, the phenotypicsignature, or the set of shape features.